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秋粮 收获进度","太和县 大豆 亩产 313.34公斤","安徽秋粮收获进度-4953","2026-10-15T00:06:03.038238Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":8,"content":233,"source_name":234,"source_url":8,"published_at":170,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":235,"score_detail":236,"sources":240,"tags":242,"primary_topic":246,"secondary_topics":247,"classification":253,"search_phrases":275,"slug":278,"view_count":21,"doi":8,"paper":8,"created_at":279},4847,"[光明网] 减药、降毒、增产!玉米绿色防控新技术亮相新乡田间","https:\u002F\u002Fcaas.cn\u002Fxwzx\u002Fmtxw\u002F3ef9b71ec5ba48b2864a2b4f63c877e4.htm","10月11日,中国农业科学院植物保护研究所、中国农业科学院中原研究中心、农业农村部农产品质量安全生物性危害因子控制重点实验室联合在河南新乡举办玉米病虫害绿色防控与控残减毒技术集成示范观摩交流会。科研团队针对当前玉米生产三大核心困境——病虫灾变防控难、传统防治弊端凸显、毒素污染风险突出,集中展示适配黄淮海夏玉米产区的全生育期病虫害绿色防控、控残减毒集成技术体系,推动田间防控从'粗放防治'向'精准绿色'转变。","金秋时节，黄淮海玉米产区迎来收获关键期，病虫害防控、农药残留管控、真菌毒素治理，直接关系玉米产量稳增与品质安全，是保障粮食安全的关键一环。\n\n10月11日，玉米病虫害绿色防控与控残减毒技术集成示范观摩交流会在河南新乡举办。本次会议由中国农业科学院[植物保护研究所](http:\u002F\u002Fipp.caas.cn\u002F)、中国农业科学院中原研究中心、农业农村部农产品质量安全生物性危害因子（植物源）控制重点实验室、北京中保绿农科技集团有限公司联合主办。来自行业主管部门、科研院所、高校的专家学者及项目骨干、种植大户代表等60名代表齐聚现场，观摩最新科研成果、共话玉米产业绿色高质量发展路径。\n\n![Image 1: image-01.jpg](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-10\u002F3a8255df19074f11916af4c08e13e1d1.jpg)  \n观摩会现场\n\n**三重难题制约玉米优质高产发展**\n\n作为我国核心粮食作物，玉米的稳产提质对保障国家粮食安全、夯实粮油供给基础至关重要。当前，我国玉米主产区病虫害呈现频发、重发态势，多重难题叠加，成为产业发展的突出瓶颈。\n\n据科研团队调研分析，目前玉米生产面临三大核心困境。一是病虫灾变防控难，南方锈病、穗腐病、玉米螟、桃蛀螟等重大病虫害灾变机制复杂，传统监测手段智能化程度不足，精准预判、提前防控能力薄弱。二是传统防治弊端凸显，长期依赖化学农药的防控模式，易造成农药残留超标、农业面源污染等问题，破坏农田生态平衡。三是毒素污染风险突出，玉米生物毒素污染呈现超标率高、复合污染率高、区域风险高的“三高”特征，严重影响玉米品质与食用安全，制约产业提质升级。\n\n针对产业痛点，中国农业科学院[植物保护研究所](http:\u002F\u002Fipp.caas.cn\u002F)牵头，联合国内优势科研单位，依托“十四五”国家重点研发计划两大核心项目，组建专项科研团队开展有组织协同攻关，聚焦病虫害演替规律、绿色防控技术、控残减毒机理三大核心方向，系统破解玉米生产稳产提质难题。\n\n**全周期绿色方案实现降本增效减毒**\n\n此次新乡观摩会上，科研团队在新乡综合实验基地集中展示了适配黄淮海夏玉米产区的全生育期病虫害绿色防控、控残减毒集成技术体系，一系列轻量化、高效化、可落地的新技术、新方案、新产品，让田间防控实现从“粗放防治”向“精准绿色”转变。\n\n在病虫害绿色防控层面，多项创新田间技术落地见效。科研团队创新构建“功能植物阻击带”模式，在玉米田四周配套种植向日葵、高粱，利用桃蛀螟等害虫的取食偏好诱集虫源，实现集中诱杀、生态控害，无需大量喷施农药即可降低虫害基数。同时创新“一包两滴法”防护体系，通过播种前种子包衣、苗期与大喇叭口关键期精准滴施药剂，构建“种子—土壤—根系”三层立体防护网络，配套自主研发的智能涌波高效水肥药一体机，实现药肥精准施用，大幅减少农药浪费与面源污染。针对玉米穗期病虫高发问题，推广无人机飞防“一喷多促”技术，一次施药即可兼治多种病虫害，通过减少植株虫伤、机械损伤，从源头降低穗腐病发病概率。\n\n![Image 2: image-02.jpg](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-10\u002F016e571e9d2e473bb0f0a4c780c61936.jpg)  \n观摩会现场\n\n在控残减毒核心技术上，科研团队厘清关键防控机理，明确虫害防治是控制玉米伏马毒素污染的核心关键。通过大量田间试验验证，种植抗虫转基因玉米，搭配吐丝期喷施40%唑醚·戊唑醇+天沐飞防助剂，对叶部病害、穗腐病的防控效果最优，降毒成效显著。团队进一步精准锁定施药窗口期，证实玉米吐丝后5至7天内科学施药，可最大程度减少虫害伤口，阻断真菌侵染路径，从源头抑制毒素生成。\n\n此外，团队创新研发“植物健康＋高效施药”协同防控模式，将纳米硒、芸苔素内酯与专用增效助剂科学配伍，形成玉米全周期“农药减量+毒素控制”一体化技术方案。经田间实测，该技术可实现农药用量较常规降低30%以上，玉米增产约10%，兼顾生态效益与经济效益。现场还重点展示了“阿泰灵”植物免疫诱抗剂、“天沐”无人机飞防助剂等核心产品，通过激活作物自身免疫能力、提升药液田间沉积利用率，为玉米绿色防控、品质升级提供硬核产品支撑。\n\n**科研赋能筑牢粮食安全绿色根基**\n\n观摩结束后，与会专家围绕玉米病虫害发生新规律、绿色防控产品迭代优化、控残减毒技术体系完善、区域化技术适配等核心议题展开深度研讨。项目团队依次汇报重点研发计划进展，详细解读玉米病虫演替规律、农残与毒素风险管控、转基因玉米抗性治理等最新科研成果。\n\n咨询专家组充分肯定了阶段性攻关成效，并提出针对性优化建议。专家组指出，当前玉米病虫防控与品质管控形势持续变化，后续需进一步强化病虫害、生物毒素风险的常态化监测预警，深耕基础机理研究；加快绿色防控产品迭代升级，推动各类技术、产品优化集成，打造适配不同生态产区的专属防控体系；同时加大技术示范推广与基层培训力度，打通科研成果落地田间的“最后一公里”。\n\n![Image 3: image-03.jpg](https:\u002F\u002Fcaas.cn\u002Fimages\u002F2026-10\u002F6ef057e0d5154e30a413e3dbf466094e.jpg)  \n观摩会现场\n\n此次观摩交流会集中展现了中国农科院[植物保护研究所](http:\u002F\u002Fipp.caas.cn\u002F)在玉米绿色防控领域的系统性科研成果，彰显了有组织科研助力粮食稳产提质、保障农产品质量安全的价值。中国农业科学院[植物保护研究所](http:\u002F\u002Fipp.caas.cn\u002F)副所长陈捷胤表示，团队将以此次观摩交流为契机，持续深耕玉米病虫害绿色防控与控残减毒技术攻关，加快成熟技术、创新模式的规模化落地推广，持续夯实玉米产业安全、绿色、高质量发展的科技根基，为保障国家粮食安全注入持久科技动能。","中国农业科学院 2026 年 10 月 13 日",88,{"impact":237,"substance":127,"depth":77,"authority":238,"freshness":194,"relevant":21,"comment":239},24,15,"国家级科研机构牵头的玉米绿色防控与控残减毒集成技术示范，数据具体、多方信源，产业指导价值高，值得进入每日精选。",[241],{"name":234,"url":231},[207,26,243,244,245],"农药减量","植保无人机","真菌毒素",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[248],{"code":204,"slug":249,"name":250,"path":251,"parent_code":252},"crop-protection","农药与绿色防控","\u002Ftopic\u002Finputs-equipment\u002Fcrop-protection","F",{"status":38,"locked":14,"version":39,"method":40,"evidence":254},{"candidates":255,"agricultural_evidence":44},[256,260,264,270],{"code":204,"score":18,"supported":44,"title":257,"abstract":258,"tags":259},[207],[207],[243,244,207],{"code":32,"score":17,"supported":44,"title":261,"abstract":262,"tags":263},[26],[26],[26],{"code":265,"score":50,"supported":44,"title":266,"abstract":267,"tags":269},"E4",[],[268],"农产品质量",[],{"code":271,"score":21,"supported":14,"title":272,"abstract":273,"tags":274},"H8",[],[],[207],[276,277],"中国农科院 玉米绿色防控 新乡","玉米控残减毒 技术集成示范","中国农科院玉米绿色防控新乡-4847","2026-10-14T00:04:07.275036Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":286,"source_url":283,"published_at":287,"category":288,"cover_url":8,"hotness":13,"is_selected":14,"score":289,"score_detail":290,"sources":292,"tags":294,"primary_topic":299,"secondary_topics":300,"classification":307,"search_phrases":329,"slug":332,"view_count":115,"doi":333,"paper":334,"created_at":350},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z","论文",63,{"impact":19,"substance":77,"depth":128,"authority":17,"freshness":194,"relevant":21,"comment":291},"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[293],{"name":286,"url":283},[295,296,297,26,298],"智慧农业","农业人工智能","机器学习","耐盐育种",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[301],{"code":302,"slug":303,"name":304,"path":305,"parent_code":306},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":38,"locked":14,"version":39,"method":40,"evidence":308},{"candidates":309,"agricultural_evidence":44},[310,317,321],{"code":302,"score":311,"supported":44,"title":312,"abstract":314,"tags":316},19,[313],"machine learning",[297,313,315],"decision support",[296,297],{"code":32,"score":238,"supported":44,"title":318,"abstract":319,"tags":320},[33],[26,33],[26],{"code":322,"score":323,"supported":44,"title":324,"abstract":325,"tags":328},"I1",7,[],[326,327],"精准农业","precision agriculture",[295],[330,331],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":333,"openalex_id":335,"authors":336,"venue":286,"cited_by_count":115,"oa_url":283,"card":343,"direction":347,"ingested_from":349},"W7221462188",[337,340],{"name":338,"orcid":339},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":341,"orcid":342},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":344,"method":345,"finding":346,"direction":347,"opportunity":348},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","农业人工智能与决策模型","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","openalex","2026-10-12T23:30:44.762497Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":8,"source_name":357,"source_url":354,"published_at":358,"category":288,"cover_url":8,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":376,"search_phrases":407,"slug":410,"view_count":21,"doi":411,"paper":412,"created_at":424},4694,"Precision Agriculture Approaches for Nutrient Management in Maize: A Comprehensive Review of Productivity, Efficiency and Economic Benefits","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjeai\u002F2026\u002Fv48i104559","Maize nutrient management is an unusually demanding target for precision agriculture because crop demand, soil nutrient supply, weather, rooting conditions and management history vary simultaneously across space and time. Uniform fertiliser programmes can therefore be efficient in one part of a field and inefficient in another, while a recommendation that is appropriate before planting may become suboptimal after rainfall, mineralisation and crop establishment have altered nutrient availability. This critical narrative review evaluates precision approaches for nutrient management in maize, with emphasis on productivity, nutrient-use efficiency and farm-level economic performance. Literature published from 1 January 1990 to 2 August 2026 was examined using agricultural and multidisciplinary scholarly sources, supplemented by citation searching and bibliographic verification. The evidence was organised around the decision chain linking measurement, diagnosis, prescription and nutrient application. Strongest empirical support exists for site-specific and in-season nitrogen management because nitrogen is mobile, strongly coupled to weather and readily expressed in canopy signals. Chlorophyll meters, active optical sensors, aerial and unmanned aerial vehicle imagery, management zones, crop models and machine-learning approaches can improve the timing or spatial targeting of nitrogen, but diagnostic accuracy does not automatically translate into an economically superior fertiliser prescription. Multi-nutrient decision-support systems broaden precision management beyond nitrogen and can improve the balance of nitrogen, phosphorus and potassium, particularly where farmer practice is poorly aligned with crop demand. Across the evidence base, yield gains are often modest when conventional fertilisation is already adequate; benefits more consistently arise through reduced excess nutrient use, improved nutrient-use efficiency, better risk management or correction of under-fertilisation. Profitability is highly conditional on field heterogeneity, fertiliser and grain prices, baseline management, technology cost, scale and weather. Future progress requires multi-year on-farm validation of complete decision chains, stronger treatment of uncertainty and multi-stress confounding, improved multi-nutrient diagnostics, and economic evaluations that include fixed, transaction and information costs. Precision nutrient management is best understood as an adaptive decision framework rather than a single sensor or variable-rate technology.","玉米养分管理对精准农业而言是一个异常苛刻的目标，因为作物需求、土壤养分供应、天气、根系条件和管理历史在空间和时间上同时变化。因此，均匀施肥方案在田块某一部分可能高效，而在另一部分则可能低效；而播种前适宜的推荐方案，在降雨、矿化和作物建成改变养分有效性之后，也可能变得次优。这篇批判性叙述综述评估了玉米养分管理的精准方法，重点关注生产力、养分利用效率和农场层面经济表现。利用农业与多学科学术来源，检索了1990年1月1日至2026年8月2日发表的文献，并以引文检索和书目核验作为补充。证据围绕连接测量、诊断、处方和养分施用的决策链进行组织。最强的实证支持存在于位点特异性和季内氮管理，因为氮具有移动性、与天气强耦合，并易于在冠层信号中表达。叶绿素仪、主动光学传感器、航空和无人机影像、管理分区、作物模型和机器学习方法可以改善氮的施用时机或空间靶向，但诊断准确性并不会自动转化为经济上更优的施肥处方。多养分决策支持系统将精准管理扩展到氮之外，并能改善氮、磷、钾的平衡，尤其是在农户实践与作物需求严重不匹配的情况下。在整个证据基础中，当常规施肥已经充足时，产量增益往往有限；效益更一致地来自减少过量养分使用、提高养分利用效率、改善风险管理或纠正施肥不足。盈利能力高度取决于田块异质性、肥料和谷物价格、基线管理、技术成本、规模和天气。未来进展需要对完整决策链进行多年农场验证，更有力地处理不确定性和多胁迫混杂，改进多养分诊断，以及纳入固定成本、交易成本和信息成本的经济评价。精准养分管理最好被理解为一个适应性决策框架","Journal of Experimental Agriculture International","2026-10-10T00:00:00Z",71,{"impact":17,"substance":78,"depth":361,"authority":129,"freshness":194,"relevant":21,"comment":362},17,"系统综述精准养分管理在玉米上的产量、效率与经济性证据，方法学扎实但属学术综述，产业落地价值需长期验证。",[364],{"name":357,"url":354},[295,366,26,326,367,368],"变量施肥","遥感监测","养分管理",{"code":32,"slug":33,"name":26,"path":34,"parent_code":35},[371],{"code":372,"slug":373,"name":374,"path":375,"parent_code":252},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers",{"status":38,"locked":14,"version":39,"method":40,"evidence":377},{"candidates":378,"agricultural_evidence":44},[379,383,387,394,398,402],{"code":322,"score":128,"supported":44,"title":380,"abstract":381,"tags":382},[327],[326,327],[295,326],{"code":32,"score":238,"supported":44,"title":384,"abstract":385,"tags":386},[33],[26,33],[26],{"code":372,"score":388,"supported":44,"title":389,"abstract":390,"tags":393},11,[],[391,392,368],"施肥","肥料",[368,366],{"code":141,"score":64,"supported":44,"title":395,"abstract":396,"tags":397},[],[153,142],[],{"code":302,"score":50,"supported":44,"title":399,"abstract":400,"tags":401},[],[297],[],{"code":403,"score":21,"supported":14,"title":404,"abstract":405,"tags":406},"I3",[],[],[367],[408,409],"玉米 精准施肥 氮肥管理","遥感 变量施肥 养分利用效率","玉米精准施肥氮肥管理-4694","10.9734\u002Fjeai\u002F2026\u002Fv48i104559",{"doi":411,"openalex_id":413,"authors":414,"venue":357,"cited_by_count":115,"oa_url":354,"card":419,"direction":347,"ingested_from":349},"W7221366291",[415,417],{"name":416,"orcid":8},"Piyali Pal",{"name":418,"orcid":8},"Disha Chattopadhyay",{"tldr":420,"method":421,"finding":422,"direction":347,"opportunity":423},"综述玉米精准养分管理在生产力、效率与经济效益上的证据与局限。","系统检索1990-2026年文献，按测量-诊断-处方-施用决策链组织证据。","氮素精准管理证据最强，但诊断精度不必然带来经济更优的施肥处方。","亟需多年多点田间验证完整决策链，并纳入不确定性、多养分诊断与信息成本的经济评价。","2026-10-11T23:30:51.450082Z"]